outlier_analyses
Potential outliers
Removed team 17080712_1 with over 600 collisions and massive distance deviation
Collisions
| team | age | age_drone | aus_born | aus_born_drone | aus_years | aus_years_drone | dic_use | dic_use_drone | eng_fl | eng_fl_drone | gender | gender_drone | collisions_overall | speed_overall | time_taken_overall | distance_overall | distance_overall_deviation | events_missed_overall | issue | issue_note |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 17032915_2 | 18 | 18 | 1 | 1 | NA | NA | 1 | NA | 2 | 1 | 2 | 2 | 589 | 5.10237 | 2462.302 | 12131.93 | 531.9305 | 5 | FALSE | NA |
| 17040314_1 | 18 | 18 | 1 | 2 | NA | 1 | NA | 1 | 1 | 2 | 1 | 2 | 370 | 10.04094 | 1263.552 | 12678.38 | 1078.3817 | 0 | FALSE | NA |
| 17080810_2 | 28 | 19 | 1 | 1 | NA | NA | NA | NA | 1 | 1 | 2 | 2 | 391 | 11.12127 | 1260.406 | 14502.92 | 2902.9222 | 0 | FALSE | NA |
## [1] "All scores on psych variables are within 1.5 SD of the mean"Speed
| team | age | age_drone | aus_born | aus_born_drone | aus_years | aus_years_drone | dic_use | dic_use_drone | eng_fl | eng_fl_drone | gender | gender_drone | collisions_overall | speed_overall | time_taken_overall | distance_overall | distance_overall_deviation | events_missed_overall | issue | issue_note |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 17040414_2 | 18 | 19 | 1 | 1 | NA | NA | NA | NA | 1 | 1 | 2 | 2 | 228 | 5.700897 | 1943.420 | 11398.96 | -201.0416 | 3 | FALSE | NA |
| 17040711_1 | 20 | 18 | 1 | 2 | NA | 17 | NA | NA | 1 | 1 | 2 | 2 | 60 | 4.360415 | 2650.336 | 11829.80 | 229.7985 | 0 | FALSE | NA |
| 17080810_2 | 28 | 19 | 1 | 1 | NA | NA | NA | NA | 1 | 1 | 2 | 2 | 391 | 11.121270 | 1260.406 | 14502.92 | 2902.9222 | 0 | FALSE | NA |
## [1] "All scores on psych variables are within 1.5 SD of the mean"Distance deviation
| team | age | age_drone | aus_born | aus_born_drone | aus_years | aus_years_drone | dic_use | dic_use_drone | eng_fl | eng_fl_drone | gender | gender_drone | collisions_overall | speed_overall | time_taken_overall | distance_overall | distance_overall_deviation | events_missed_overall | issue | issue_note |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16101114_1 | 18 | 18 | 2 | 1 | 12 | NA | NA | NA | 1 | 1 | 1 | 2 | 119 | 8.843025 | 1929.837 | 16970.16 | 5370.1591 | 1 | FALSE | NA |
| 16110214_1 | 18 | 19 | 2 | 2 | 13 | 1 | 2 | 3 | 2 | 2 | 1 | 2 | 208 | 7.894563 | 2110.132 | 17066.86 | 5466.8634 | 2 | FALSE | NA |
| 17032409_1 | 25 | 18 | 1 | 1 | NA | NA | 2 | NA | 2 | 1 | 2 | 2 | 234 | 7.476407 | 2187.731 | 16259.47 | 4659.4685 | 0 | FALSE | NA |
| 17081512_2 | 18 | 19 | 1 | 2 | NA | 5 | NA | 3 | 1 | 2 | 1 | 2 | 291 | 9.974998 | 1252.345 | 12589.61 | 989.6076 | 0 | TRUE | major driver-drone networking error - codriver saw no-go signs in place of all arrows and saw roughly 10% of the driver’s traffic |
## [1] "All scores on psych variables are within 1.5 SD of the mean"Collisions analyses with outliers removed
## [1] "Team 17032915_2 removed from analyses"
## # A tibble: 8 x 2
## rowname collisions_overall
## <chr> <dbl>
## 1 confidence_drone 0.28
## 2 incongruent_errors 0.28
## 3 inhibitory_cost 0.27
## 4 prop_female 0.31
## 5 sit.awareness -0.34
## 6 sit.awareness_driver -0.31
## 7 switch_errors 0.38
## 8 terrible_codriver 0.37
## vars n mean sd median trimmed mad min max range
## confidence_drone 1 52 0.00 1.00 0.00 0.04 0.89 -2.75 1.75 4.50
## incongruent_errors 2 52 0.00 1.00 -0.30 -0.16 0.86 -0.88 3.19 4.07
## inhibitory_cost 3 52 0.00 1.00 -0.12 -0.07 0.79 -2.31 2.87 5.18
## prop_female* 4 52 2.31 0.67 2.00 2.38 1.48 1.00 3.00 2.00
## switch_errors 5 52 0.00 1.00 -0.08 -0.14 0.74 -1.07 3.40 4.47
## terrible_codriver 6 52 0.00 1.00 -0.17 -0.15 0.75 -1.34 3.55 4.89
## skew kurtosis se
## confidence_drone -0.34 -0.07 0.14
## incongruent_errors 1.29 1.02 0.14
## inhibitory_cost 0.72 1.08 0.14
## prop_female* -0.43 -0.86 0.09
## switch_errors 1.14 1.28 0.14
## terrible_codriver 1.57 2.73 0.14
##
## Call:
## lm(formula = tmp$collisions_overall ~ ., data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -131.621 -38.377 -1.337 35.032 162.174
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102.22 30.05 3.402 0.00144 **
## confidence_drone 23.94 10.68 2.242 0.03005 *
## incongruent_errors 20.88 11.29 1.848 0.07129 .
## inhibitory_cost 22.32 10.21 2.187 0.03407 *
## prop_female0.5 56.75 33.86 1.676 0.10085
## prop_female1 68.16 34.13 1.997 0.05204 .
## switch_errors 26.80 11.05 2.426 0.01945 *
## terrible_codriver 26.33 10.42 2.528 0.01515 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 71.75 on 44 degrees of freedom
## Multiple R-squared: 0.486, Adjusted R-squared: 0.4042
## F-statistic: 5.942 on 7 and 44 DF, p-value: 6.695e-05
##
## Correlations
## ----------------------------------------------------
## Variable Zero Order Partial Part
## ----------------------------------------------------
## confidence_drone 0.276 0.320 0.242
## incongruent_errors 0.280 0.268 0.200
## inhibitory_cost 0.274 0.313 0.236
## prop_female0.5 -0.036 0.245 0.181
## prop_female1 0.227 0.288 0.216
## switch_errors 0.380 0.343 0.262
## terrible_codriver 0.370 0.356 0.273
## ----------------------------------------------------
##
## Call:
## omcdiag(x = x, y = y)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.7269 0
## Farrar Chi-Square: 15.3641 0
## Red Indicator: 0.1397 0
## Sum of Lambda Inverse: 6.7053 0
## Theil's Method: -1.7594 0
## Condition Number: 12.4171 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = x, y = y)
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein
## confidence_drone 1.1086 0.9021 0.9989 1.2758 0.9498 1.4801 0
## incongruent_errors 1.1910 0.8396 1.7576 2.2447 0.9163 1.5902 0
## inhibitory_cost 1.0299 0.9710 0.2750 0.3512 0.9854 1.3750 0
## prop_female 1.0963 0.9122 0.8856 1.1311 0.9551 1.4636 0
## switch_errors 1.2051 0.8298 1.8872 2.4103 0.9109 1.6090 0
## terrible_codriver 1.0744 0.9307 0.6846 0.8743 0.9647 1.4345 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## prop_female , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.4748
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [1] "Team 17040314_1 removed from analyses"
## # A tibble: 9 x 2
## rowname collisions_overall
## <chr> <dbl>
## 1 incongruent_errors 0.36
## 2 inconsistent_codriver 0.28
## 3 inhibitory_cost 0.26
## 4 prop_female 0.37
## 5 repeat_errors 0.26
## 6 sex_driver -0.33
## 7 sit.awareness -0.39
## 8 sit.awareness_driver -0.4
## 9 terrible_codriver 0.3
## vars n mean sd median trimmed mad min max
## incongruent_errors 1 52 0.00 1.00 -0.31 -0.17 0.83 -0.88 3.06
## inconsistent_codriver 2 52 0.00 1.00 -0.17 -0.08 1.04 -1.36 2.49
## inhibitory_cost 3 52 0.00 1.00 -0.10 -0.07 0.76 -2.32 2.87
## prop_female* 4 52 2.33 0.68 2.00 2.40 1.48 1.00 3.00
## repeat_errors 5 52 0.00 1.00 0.10 -0.12 0.89 -1.10 2.51
## terrible_codriver 6 52 0.00 1.00 -0.16 -0.16 0.73 -1.34 3.59
## range skew kurtosis se
## incongruent_errors 3.94 1.25 0.71 0.14
## inconsistent_codriver 3.84 0.61 -0.52 0.14
## inhibitory_cost 5.18 0.70 1.07 0.14
## prop_female* 2.00 -0.48 -0.85 0.09
## repeat_errors 3.60 0.84 0.13 0.14
## terrible_codriver 4.93 1.64 3.00 0.14
##
## Call:
## lm(formula = tmp$collisions_overall ~ ., data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -146.384 -42.882 -7.766 37.663 305.942
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 99.802 37.492 2.662 0.0108 *
## incongruent_errors 34.159 15.359 2.224 0.0313 *
## inconsistent_codriver 13.420 13.631 0.984 0.3303
## inhibitory_cost 22.092 12.583 1.756 0.0861 .
## prop_female0.5 44.930 42.709 1.052 0.2985
## prop_female1 94.473 41.969 2.251 0.0294 *
## repeat_errors 7.677 15.057 0.510 0.6127
## terrible_codriver 22.137 13.470 1.643 0.1074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 89.27 on 44 degrees of freedom
## Multiple R-squared: 0.3964, Adjusted R-squared: 0.3004
## F-statistic: 4.128 on 7 and 44 DF, p-value: 0.001451
##
## Correlations
## -------------------------------------------------------
## Variable Zero Order Partial Part
## -------------------------------------------------------
## incongruent_errors 0.359 0.318 0.260
## inconsistent_codriver 0.285 0.147 0.115
## inhibitory_cost 0.257 0.256 0.206
## prop_female0.5 -0.145 0.157 0.123
## prop_female1 0.319 0.321 0.264
## repeat_errors 0.259 0.077 0.060
## terrible_codriver 0.303 0.240 0.192
## -------------------------------------------------------
##
## Call:
## omcdiag(x = x, y = y)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.5942 0
## Farrar Chi-Square: 25.0725 1
## Red Indicator: 0.1780 0
## Sum of Lambda Inverse: 7.1863 0
## Theil's Method: -1.0709 0
## Condition Number: 7.7182 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = x, y = y)
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein
## incongruent_errors 1.3875 0.7207 3.5649 4.5530 0.8490 1.9387 0
## inconsistent_codriver 1.1572 0.8641 1.4466 1.8476 0.9296 1.6170 0
## inhibitory_cost 1.0115 0.9886 0.1060 0.1354 0.9943 1.4134 0
## prop_female 1.0563 0.9467 0.5176 0.6610 0.9730 1.4759 0
## repeat_errors 1.4128 0.7078 3.7974 4.8499 0.8413 1.9740 0
## terrible_codriver 1.1610 0.8613 1.4811 1.8917 0.9281 1.6222 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## inconsistent_codriver , inhibitory_cost , repeat_errors , terrible_codriver , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.3963
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [1] "Team 17080810_2 removed from analyses"
## # A tibble: 7 x 2
## rowname collisions_overall
## <chr> <dbl>
## 1 incongruent_errors 0.43
## 2 inconsistent_codriver 0.290
## 3 prop_female 0.31
## 4 repeat_errors 0.27
## 5 sit.awareness -0.35
## 6 sit.awareness_driver -0.38
## 7 terrible_codriver 0.32
## vars n mean sd median trimmed mad min max
## incongruent_errors 1 52 0.00 1.00 -0.35 -0.16 0.84 -0.91 3.04
## inconsistent_codriver 2 52 0.00 1.00 -0.17 -0.08 1.04 -1.36 2.48
## prop_female* 3 52 2.31 0.67 2.00 2.38 1.48 1.00 3.00
## repeat_errors 4 52 0.00 1.00 0.10 -0.12 0.89 -1.10 2.51
## terrible_codriver 5 52 0.00 1.00 -0.17 -0.15 0.73 -1.34 3.55
## range skew kurtosis se
## incongruent_errors 3.95 1.18 0.57 0.14
## inconsistent_codriver 3.84 0.60 -0.54 0.14
## prop_female* 2.00 -0.43 -0.86 0.09
## repeat_errors 3.60 0.84 0.13 0.14
## terrible_codriver 4.89 1.58 2.77 0.14
##
## Call:
## lm(formula = tmp$collisions_overall ~ ., data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -129.67 -42.39 -14.54 37.62 299.49
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103.733 37.188 2.789 0.00771 **
## incongruent_errors 41.666 14.903 2.796 0.00759 **
## inconsistent_codriver 14.065 13.514 1.041 0.30353
## prop_female0.5 47.051 42.316 1.112 0.27208
## prop_female1 84.166 41.609 2.023 0.04906 *
## repeat_errors 5.313 14.550 0.365 0.71671
## terrible_codriver 26.375 13.260 1.989 0.05279 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 88.48 on 45 degrees of freedom
## Multiple R-squared: 0.3839, Adjusted R-squared: 0.3017
## F-statistic: 4.673 on 6 and 45 DF, p-value: 0.0009001
##
## Correlations
## -------------------------------------------------------
## Variable Zero Order Partial Part
## -------------------------------------------------------
## incongruent_errors 0.431 0.385 0.327
## inconsistent_codriver 0.294 0.153 0.122
## prop_female0.5 -0.065 0.164 0.130
## prop_female1 0.242 0.289 0.237
## repeat_errors 0.265 0.054 0.043
## terrible_codriver 0.324 0.284 0.233
## -------------------------------------------------------
##
## Call:
## omcdiag(x = x, y = y)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.6418 0
## Farrar Chi-Square: 21.2859 1
## Red Indicator: 0.2038 0
## Sum of Lambda Inverse: 5.9820 0
## Theil's Method: -0.7487 0
## Condition Number: 5.8572 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = x, y = y)
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein
## incongruent_errors 1.3135 0.7613 3.6835 5.0158 0.8725 1.7729 0
## inconsistent_codriver 1.1585 0.8632 1.8629 2.5367 0.9291 1.5637 0
## prop_female 1.0383 0.9631 0.4496 0.6122 0.9814 1.4014 0
## repeat_errors 1.3270 0.7536 3.8419 5.2315 0.8681 1.7911 0
## terrible_codriver 1.1447 0.8736 1.7002 2.3152 0.9347 1.5450 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## inconsistent_codriver , repeat_errors , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.3835
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## NULLSpeed analyses with outliers removed
## [1] "Team 17040414_2 removed from analyses"
## # A tibble: 12 x 2
## rowname speed_overall
## <chr> <dbl>
## 1 agreeableness -0.3
## 2 helpful_exchange -0.46
## 3 inconsistent_codriver -0.32
## 4 leadership -0.33
## 5 leadership_co_driver -0.45
## 6 neuroticism_drone 0.31
## 7 prop_female -0.47
## 8 resilience_drone -0.27
## 9 sex_driver 0.570
## 10 sit.awareness 0.28
## 11 sit.awareness_driver 0.570
## 12 switch_time_drone 0.3
## vars n mean sd median trimmed mad min max
## agreeableness 1 52 0.00 1.00 0.05 0.05 0.87 -2.31 1.61
## helpful_exchange 2 52 0.00 1.00 0.05 -0.03 1.08 -1.82 2.44
## inconsistent_codriver 3 52 0.00 1.00 -0.06 -0.07 1.22 -1.40 2.49
## neuroticism_drone 4 52 0.00 1.00 -0.03 -0.03 1.02 -2.09 2.39
## prop_female* 5 52 2.31 0.67 2.00 2.38 1.48 1.00 3.00
## resilience_drone 6 46 0.00 1.00 0.07 0.00 1.16 -1.86 2.20
## switch_time_drone 7 52 0.00 1.00 -0.26 -0.08 0.94 -1.50 2.36
## range skew kurtosis se
## agreeableness 3.92 -0.38 -0.19 0.14
## helpful_exchange 4.26 0.21 -0.54 0.14
## inconsistent_codriver 3.89 0.58 -0.50 0.14
## neuroticism_drone 4.48 0.35 -0.53 0.14
## prop_female* 2.00 -0.43 -0.86 0.09
## resilience_drone 4.06 0.06 -0.69 0.15
## switch_time_drone 3.86 0.64 -0.64 0.14
##
## Call:
## lm(formula = tmp$speed_overall ~ ., data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.99863 -0.57799 0.06526 0.70773 2.62132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.18305 0.60697 15.129 <2e-16 ***
## agreeableness -0.06200 0.21294 -0.291 0.7725
## helpful_exchange -0.45523 0.21652 -2.102 0.0424 *
## inconsistent_codriver -0.24358 0.19376 -1.257 0.2166
## neuroticism_drone 0.26026 0.21349 1.219 0.2305
## prop_female0.5 -0.30897 0.66618 -0.464 0.6455
## prop_female1 -1.34691 0.66638 -2.021 0.0505 .
## resilience_drone -0.06134 0.19979 -0.307 0.7605
## switch_time_drone 0.34781 0.19698 1.766 0.0857 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 37 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.531, Adjusted R-squared: 0.4296
## F-statistic: 5.236 on 8 and 37 DF, p-value: 0.0002035
##
## Correlations
## --------------------------------------------------------
## Variable Zero Order Partial Part
## --------------------------------------------------------
## agreeableness -0.305 -0.048 -0.033
## helpful_exchange -0.501 -0.327 -0.237
## inconsistent_codriver -0.353 -0.202 -0.142
## neuroticism_drone 0.343 0.197 0.137
## prop_female0.5 0.439 -0.076 -0.052
## prop_female1 -0.539 -0.315 -0.228
## resilience_drone -0.270 -0.050 -0.035
## switch_time_drone 0.296 0.279 0.199
## --------------------------------------------------------
##
## Call:
## omcdiag(x = x, y = y)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.3880 0
## Farrar Chi-Square: 40.0307 1
## Red Indicator: 0.2275 0
## Sum of Lambda Inverse: 8.9938 0
## Theil's Method: -1.6473 0
## Condition Number: 42.7239 1
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = x, y = y)
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein
## agreeableness 1.4488 0.6902 2.9173 3.5905 0.8308 208.6639 0
## helpful_exchange 1.5373 0.6505 3.4926 4.2986 0.8065 221.4121 0
## inconsistent_codriver 1.1935 0.8379 1.2576 1.5478 0.9154 171.8881 0
## neuroticism_drone 1.3000 0.7692 1.9500 2.4000 0.8771 187.2312 0
## prop_female 1.1318 0.8835 0.8569 1.0547 0.9400 163.0115 0
## resilience_drone 1.2438 0.8040 1.5847 1.9504 0.8967 179.1363 0
## switch_time_drone 1.1385 0.8783 0.9003 1.1081 0.9372 163.9730 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## agreeableness , inconsistent_codriver , neuroticism_drone , resilience_drone , switch_time_drone , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.5223
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [1] "Team 17040711_1 removed from analyses"
## # A tibble: 11 x 2
## rowname speed_overall
## <chr> <dbl>
## 1 agreeableness -0.35
## 2 helpful_exchange -0.42
## 3 inconsistent_codriver -0.31
## 4 leadership -0.290
## 5 leadership_co_driver -0.31
## 6 prop_female -0.47
## 7 repeat_time_drone 0.31
## 8 sex_driver 0.580
## 9 sit.awareness 0.39
## 10 sit.awareness_driver 0.570
## 11 switch_time_drone 0.32
## vars n mean sd median trimmed mad min max
## agreeableness 1 52 0.00 1.00 0.02 0.06 1.15 -2.30 1.57
## helpful_exchange 2 52 0.00 1.00 0.01 -0.03 1.01 -1.78 2.44
## inconsistent_codriver 3 52 0.00 1.00 -0.05 -0.07 1.23 -1.37 2.47
## prop_female* 4 52 2.31 0.67 2.00 2.38 1.48 1.00 3.00
## repeat_time_drone 5 52 0.00 1.00 -0.25 -0.12 0.90 -1.49 3.23
## switch_time_drone 6 52 0.00 1.00 -0.32 -0.08 0.87 -1.49 2.36
## range skew kurtosis se
## agreeableness 3.87 -0.40 -0.27 0.14
## helpful_exchange 4.22 0.26 -0.59 0.14
## inconsistent_codriver 3.85 0.58 -0.55 0.14
## prop_female* 2.00 -0.43 -0.86 0.09
## repeat_time_drone 4.71 1.04 0.68 0.14
## switch_time_drone 3.85 0.65 -0.64 0.14
##
## Call:
## lm(formula = tmp$speed_overall ~ ., data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3104 -0.6196 0.0890 0.7295 3.3269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.0952 0.4830 18.830 <2e-16 ***
## agreeableness -0.1800 0.1959 -0.919 0.3633
## helpful_exchange -0.3715 0.1907 -1.948 0.0578 .
## inconsistent_codriver -0.1775 0.1759 -1.009 0.3184
## prop_female0.5 -0.2820 0.5592 -0.504 0.6166
## prop_female1 -1.2830 0.5408 -2.372 0.0221 *
## repeat_time_drone -0.1819 0.3527 -0.516 0.6087
## switch_time_drone 0.5478 0.3354 1.633 0.1095
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.154 on 44 degrees of freedom
## Multiple R-squared: 0.4611, Adjusted R-squared: 0.3753
## F-statistic: 5.378 on 7 and 44 DF, p-value: 0.0001682
##
## Correlations
## --------------------------------------------------------
## Variable Zero Order Partial Part
## --------------------------------------------------------
## agreeableness -0.353 -0.137 -0.102
## helpful_exchange -0.421 -0.282 -0.216
## inconsistent_codriver -0.310 -0.150 -0.112
## prop_female0.5 0.419 -0.076 -0.056
## prop_female1 -0.531 -0.337 -0.263
## repeat_time_drone 0.307 -0.078 -0.057
## switch_time_drone 0.317 0.239 0.181
## --------------------------------------------------------
##
## Call:
## omcdiag(x = x, y = y)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.1423 0
## Farrar Chi-Square: 93.9006 1
## Red Indicator: 0.2993 0
## Sum of Lambda Inverse: 13.6096 0
## Theil's Method: 0.0920 0
## Condition Number: 26.8449 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = x, y = y)
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein
## agreeableness 1.4711 0.6798 4.3340 5.5353 0.8245 4.3982 0
## helpful_exchange 1.3590 0.7359 3.3025 4.2179 0.8578 4.0630 0
## inconsistent_codriver 1.1782 0.8488 1.6390 2.0933 0.9213 3.5224 0
## prop_female 1.0891 0.9182 0.8193 1.0463 0.9582 3.2560 0
## repeat_time_drone 4.2916 0.2330 30.2827 38.6763 0.4827 12.8307 1
## switch_time_drone 4.2207 0.2369 29.6308 37.8437 0.4867 12.6189 1
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## agreeableness , inconsistent_codriver , repeat_time_drone , switch_time_drone , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.4511
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [1] "Team 17080810_2 removed from analyses"
## # A tibble: 11 x 2
## rowname speed_overall
## <chr> <dbl>
## 1 agreeableness -0.39
## 2 helpful_exchange -0.39
## 3 inconsistent_codriver -0.28
## 4 leadership -0.290
## 5 leadership_co_driver -0.35
## 6 prop_female -0.55
## 7 repeat_time_drone 0.31
## 8 sex_driver 0.65
## 9 sit.awareness 0.41
## 10 sit.awareness_driver 0.65
## 11 switch_time_drone 0.34
## vars n mean sd median trimmed mad min max
## agreeableness 1 52 0.00 1.00 0.05 0.05 0.87 -2.31 1.61
## helpful_exchange 2 52 0.00 1.00 0.05 -0.03 1.08 -1.81 2.43
## inconsistent_codriver 3 52 0.00 1.00 -0.17 -0.08 1.04 -1.36 2.48
## prop_female* 4 52 2.31 0.67 2.00 2.38 1.48 1.00 3.00
## repeat_time_drone 5 52 0.00 1.00 -0.27 -0.11 0.90 -1.53 3.23
## switch_time_drone 6 52 0.00 1.00 -0.26 -0.08 0.94 -1.50 2.36
## range skew kurtosis se
## agreeableness 3.92 -0.38 -0.19 0.14
## helpful_exchange 4.24 0.21 -0.56 0.14
## inconsistent_codriver 3.84 0.60 -0.54 0.14
## prop_female* 2.00 -0.43 -0.86 0.09
## repeat_time_drone 4.76 1.01 0.68 0.14
## switch_time_drone 3.86 0.64 -0.64 0.14
##
## Call:
## lm(formula = tmp$speed_overall ~ ., data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8337 -0.6767 0.1897 0.7843 1.8078
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.0702 0.4677 19.395 < 2e-16 ***
## agreeableness -0.3170 0.1907 -1.663 0.10350
## helpful_exchange -0.2320 0.1883 -1.232 0.22440
## inconsistent_codriver -0.1236 0.1697 -0.728 0.47045
## prop_female0.5 -0.2331 0.5395 -0.432 0.66778
## prop_female1 -1.5847 0.5242 -3.023 0.00416 **
## repeat_time_drone -0.2637 0.3325 -0.793 0.43198
## switch_time_drone 0.6297 0.3194 1.972 0.05495 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.115 on 44 degrees of freedom
## Multiple R-squared: 0.5327, Adjusted R-squared: 0.4584
## F-statistic: 7.167 on 7 and 44 DF, p-value: 1.013e-05
##
## Correlations
## --------------------------------------------------------
## Variable Zero Order Partial Part
## --------------------------------------------------------
## agreeableness -0.390 -0.243 -0.171
## helpful_exchange -0.394 -0.183 -0.127
## inconsistent_codriver -0.281 -0.109 -0.075
## prop_female0.5 0.484 -0.065 -0.045
## prop_female1 -0.613 -0.415 -0.312
## repeat_time_drone 0.315 -0.119 -0.082
## switch_time_drone 0.337 0.285 0.203
## --------------------------------------------------------
##
## Call:
## omcdiag(x = x, y = y)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.1422 0
## Farrar Chi-Square: 93.9611 1
## Red Indicator: 0.3007 0
## Sum of Lambda Inverse: 13.4487 0
## Theil's Method: -0.1784 0
## Condition Number: 26.8407 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = x, y = y)
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein
## agreeableness 1.4926 0.6700 4.5319 5.7881 0.8185 7.1622 0
## helpful_exchange 1.4143 0.7071 3.8118 4.8683 0.8409 6.7866 0
## inconsistent_codriver 1.1768 0.8497 1.6268 2.0777 0.9218 5.6469 0
## prop_female 1.0884 0.9187 0.8137 1.0393 0.9585 5.2229 0
## repeat_time_drone 4.1511 0.2409 28.9899 37.0251 0.4908 19.9187 1
## switch_time_drone 4.1254 0.2424 28.7539 36.7238 0.4923 19.7957 1
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## agreeableness , helpful_exchange , inconsistent_codriver , repeat_time_drone , switch_time_drone , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.5099
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## NULLDistance deviation analyses with outliers removed
## [1] "Team 16101114_1 removed from analyses"
## # A tibble: 7 x 2
## rowname distance_overall_deviation
## <chr> <dbl>
## 1 age_co_driver 0.26
## 2 confidence 0.34
## 3 driving_years_drone 0.27
## 4 inconsistent_codriver 0.31
## 5 neuroticism 0.31
## 6 switch_cost -0.34
## 7 terrible_codriver 0.5
## vars n mean sd median trimmed mad min max range
## age_co_driver 1 52 0 1 -0.31 -0.21 0.27 -0.49 6.12 6.61
## confidence 2 52 0 1 -0.01 0.03 0.77 -2.71 1.83 4.55
## driving_years_drone 3 52 0 1 -0.23 -0.19 0.29 -0.43 6.38 6.80
## inconsistent_codriver 4 52 0 1 -0.05 -0.07 1.23 -1.38 2.48 3.85
## neuroticism 5 52 0 1 -0.09 0.03 1.27 -2.15 1.62 3.77
## switch_cost 6 52 0 1 -0.15 -0.05 0.98 -2.30 2.53 4.82
## terrible_codriver 7 52 0 1 -0.16 -0.15 0.73 -1.37 3.55 4.93
## skew kurtosis se
## age_co_driver 4.78 24.89 0.14
## confidence -0.36 0.35 0.14
## driving_years_drone 5.20 29.32 0.14
## inconsistent_codriver 0.57 -0.53 0.14
## neuroticism -0.11 -0.98 0.14
## switch_cost 0.37 -0.35 0.14
## terrible_codriver 1.56 2.77 0.14
##
## Call:
## lm(formula = tmp$distance_overall_deviation ~ ., data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1840.1 -621.5 -144.5 566.3 2990.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 878.1 138.0 6.364 9.84e-08 ***
## age_co_driver -198.5 467.0 -0.425 0.6729
## confidence 287.9 151.6 1.899 0.0641 .
## driving_years_drone 545.7 472.2 1.156 0.2541
## inconsistent_codriver 131.8 153.1 0.861 0.3940
## neuroticism 397.3 148.2 2.680 0.0103 *
## switch_cost -225.8 158.6 -1.423 0.1617
## terrible_codriver 730.4 156.2 4.676 2.79e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 995 on 44 degrees of freedom
## Multiple R-squared: 0.5665, Adjusted R-squared: 0.4975
## F-statistic: 8.215 on 7 and 44 DF, p-value: 2.239e-06
##
## Correlations
## --------------------------------------------------------
## Variable Zero Order Partial Part
## --------------------------------------------------------
## age_co_driver 0.260 -0.064 -0.042
## confidence 0.344 0.275 0.188
## driving_years_drone 0.269 0.172 0.115
## inconsistent_codriver 0.314 0.129 0.085
## neuroticism 0.305 0.375 0.266
## switch_cost -0.336 -0.210 -0.141
## terrible_codriver 0.496 0.576 0.464
## --------------------------------------------------------
##
## Call:
## omcdiag(x = x, y = y)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.0585 0
## Farrar Chi-Square: 137.0310 1
## Red Indicator: 0.2517 0
## Sum of Lambda Inverse: 28.7970 0
## Theil's Method: -0.6986 0
## Condition Number: 36.5009 1
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = x, y = y)
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein
## age_co_driver 11.2337 0.0890 76.7524 94.1496 0.2984 25.4736 1
## confidence 1.1838 0.8448 1.3782 1.6906 0.9191 2.6843 0
## driving_years_drone 11.4870 0.0871 78.6526 96.4805 0.2951 26.0481 1
## inconsistent_codriver 1.2076 0.8281 1.5570 1.9099 0.9100 2.7384 0
## neuroticism 1.1319 0.8834 0.9896 1.2139 0.9399 2.5668 0
## switch_cost 1.2963 0.7714 2.2225 2.7263 0.8783 2.9396 0
## terrible_codriver 1.2567 0.7957 1.9251 2.3615 0.8920 2.8497 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## age_co_driver , confidence , driving_years_drone , inconsistent_codriver , switch_cost , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.5665
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [1] "Team 16110214_1 removed from analyses"
## # A tibble: 5 x 2
## rowname distance_overall_deviation
## <chr> <dbl>
## 1 driving_years_drone 0.27
## 2 incongruent_errors 0.35
## 3 neuroticism 0.43
## 4 switch_cost -0.4
## 5 wm_accuracy_drone -0.26
## vars n mean sd median trimmed mad min max range
## driving_years_drone 1 52 0 1 -0.23 -0.19 0.29 -0.43 6.37 6.80
## incongruent_errors 2 52 0 1 -0.33 -0.16 0.83 -0.89 3.03 3.92
## neuroticism 3 52 0 1 0.04 0.03 1.24 -2.14 1.55 3.69
## switch_cost 4 52 0 1 -0.34 -0.06 1.01 -2.27 2.54 4.82
## wm_accuracy_drone 5 52 0 1 -0.10 -0.04 0.86 -2.24 2.42 4.67
## skew kurtosis se
## driving_years_drone 5.20 29.29 0.14
## incongruent_errors 1.17 0.54 0.14
## neuroticism -0.14 -1.02 0.14
## switch_cost 0.42 -0.33 0.14
## wm_accuracy_drone 0.35 -0.33 0.14
##
## Call:
## lm(formula = tmp$distance_overall_deviation ~ ., data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2304.3 -888.5 37.7 597.0 3258.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 876.3 163.9 5.345 2.74e-06 ***
## driving_years_drone 141.1 190.6 0.740 0.4629
## incongruent_errors 188.7 199.1 0.948 0.3483
## neuroticism 440.6 174.8 2.521 0.0152 *
## switch_cost -325.8 184.4 -1.767 0.0839 .
## wm_accuracy_drone -280.7 171.5 -1.636 0.1086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1182 on 46 degrees of freedom
## Multiple R-squared: 0.3547, Adjusted R-squared: 0.2846
## F-statistic: 5.057 on 5 and 46 DF, p-value: 0.0008929
##
## Correlations
## ------------------------------------------------------
## Variable Zero Order Partial Part
## ------------------------------------------------------
## driving_years_drone 0.267 0.109 0.088
## incongruent_errors 0.355 0.138 0.112
## neuroticism 0.427 0.348 0.299
## switch_cost -0.400 -0.252 -0.209
## wm_accuracy_drone -0.258 -0.235 -0.194
## ------------------------------------------------------
##
## Call:
## omcdiag(x = x, y = y)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.5771 0
## Farrar Chi-Square: 26.3906 1
## Red Indicator: 0.2389 0
## Sum of Lambda Inverse: 6.2015 0
## Theil's Method: -0.4988 0
## Condition Number: 13.1878 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = x, y = y)
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein
## driving_years_drone 1.3252 0.7546 3.8211 5.2033 0.8687 2.1736 0
## incongruent_errors 1.4468 0.6912 5.2505 7.1496 0.8314 2.3732 0
## neuroticism 1.1152 0.8967 1.3538 1.8435 0.9469 1.8292 0
## switch_cost 1.2409 0.8059 2.8307 3.8546 0.8977 2.0354 0
## wm_accuracy_drone 1.0734 0.9317 0.8620 1.1738 0.9652 1.7606 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## driving_years_drone , incongruent_errors , switch_cost , wm_accuracy_drone , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.3547
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [1] "Team 17032409_1 removed from analyses"
## # A tibble: 8 x 2
## rowname distance_overall_deviation
## <chr> <dbl>
## 1 confidence 0.3
## 2 congruent_time -0.26
## 3 driving_years_drone 0.26
## 4 incongruent_errors 0.34
## 5 inconsistent_codriver 0.28
## 6 neuroticism 0.33
## 7 switch_cost -0.26
## 8 terrible_codriver 0.42
## vars n mean sd median trimmed mad min max range
## confidence 1 52 0 1 0.00 0.03 0.80 -2.69 1.83 4.52
## congruent_time 2 52 0 1 -0.17 -0.08 0.89 -1.61 2.81 4.42
## driving_years_drone 3 52 0 1 -0.23 -0.19 0.29 -0.43 6.37 6.80
## incongruent_errors 4 52 0 1 -0.33 -0.16 0.83 -0.89 3.03 3.92
## inconsistent_codriver 5 52 0 1 -0.04 -0.07 1.23 -1.37 2.47 3.84
## neuroticism 6 52 0 1 -0.10 0.02 1.26 -2.13 1.60 3.73
## switch_cost 7 52 0 1 -0.20 -0.08 0.98 -1.39 2.63 4.02
## terrible_codriver 8 52 0 1 -0.17 -0.15 0.75 -1.34 3.55 4.89
## skew kurtosis se
## confidence -0.34 0.27 0.14
## congruent_time 0.78 0.00 0.14
## driving_years_drone 5.20 29.29 0.14
## incongruent_errors 1.17 0.54 0.14
## inconsistent_codriver 0.58 -0.55 0.14
## neuroticism -0.09 -0.97 0.14
## switch_cost 0.60 -0.48 0.14
## terrible_codriver 1.57 2.72 0.14
##
## Call:
## lm(formula = tmp$distance_overall_deviation ~ ., data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2044.0 -646.8 -111.1 529.2 4065.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 891.78 156.83 5.686 1.04e-06 ***
## confidence 340.86 219.22 1.555 0.127315
## congruent_time 20.26 251.62 0.081 0.936191
## driving_years_drone 215.40 182.30 1.182 0.243860
## incongruent_errors 286.17 228.52 1.252 0.217245
## inconsistent_codriver 129.20 173.81 0.743 0.461332
## neuroticism 474.37 171.01 2.774 0.008160 **
## switch_cost -12.42 201.24 -0.062 0.951081
## terrible_codriver 610.40 172.21 3.544 0.000963 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1131 on 43 degrees of freedom
## Multiple R-squared: 0.483, Adjusted R-squared: 0.3868
## F-statistic: 5.021 on 8 and 43 DF, p-value: 0.0001947
##
## Correlations
## --------------------------------------------------------
## Variable Zero Order Partial Part
## --------------------------------------------------------
## confidence 0.299 0.231 0.170
## congruent_time -0.260 0.012 0.009
## driving_years_drone 0.255 0.177 0.130
## incongruent_errors 0.339 0.188 0.137
## inconsistent_codriver 0.280 0.113 0.082
## neuroticism 0.327 0.390 0.304
## switch_cost -0.256 -0.009 -0.007
## terrible_codriver 0.417 0.476 0.389
## --------------------------------------------------------
##
## Call:
## omcdiag(x = x, y = y)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.1615 0
## Farrar Chi-Square: 88.2044 1
## Red Indicator: 0.2489 0
## Sum of Lambda Inverse: 13.0175 0
## Theil's Method: -0.6859 0
## Condition Number: 49.8746 1
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = x, y = y)
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein
## confidence 1.9165 0.5218 5.7608 6.8737 0.7223 4.1601 0
## congruent_time 2.5248 0.3961 9.5845 11.4360 0.6293 5.4806 1
## driving_years_drone 1.3252 0.7546 2.0443 2.4393 0.8687 2.8767 0
## incongruent_errors 2.0825 0.4802 6.8042 8.1187 0.6930 4.5204 1
## inconsistent_codriver 1.2047 0.8301 1.2867 1.5353 0.9111 2.6150 0
## neuroticism 1.1662 0.8575 1.0448 1.2467 0.9260 2.5315 0
## switch_cost 1.6149 0.6192 3.8652 4.6118 0.7869 3.5055 0
## terrible_codriver 1.1827 0.8456 1.1481 1.3699 0.9195 2.5672 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## confidence , congruent_time , driving_years_drone , incongruent_errors , inconsistent_codriver , switch_cost , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.483
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [1] "Team 17081512_2 removed from analyses"
## # A tibble: 5 x 2
## rowname distance_overall_deviation
## <chr> <dbl>
## 1 confidence 0.26
## 2 incongruent_errors 0.3
## 3 neuroticism 0.37
## 4 switch_cost -0.34
## 5 terrible_codriver 0.4
## vars n mean sd median trimmed mad min max range
## confidence 1 52 0 1 -0.03 0.02 0.78 -2.67 1.84 4.51
## incongruent_errors 2 52 0 1 -0.32 -0.16 0.83 -0.88 3.04 3.92
## neuroticism 3 52 0 1 -0.10 0.02 1.26 -2.13 1.60 3.73
## switch_cost 4 52 0 1 -0.17 -0.05 0.94 -2.33 2.54 4.86
## terrible_codriver 5 52 0 1 -0.15 -0.15 0.74 -1.34 3.63 4.97
## skew kurtosis se
## confidence -0.31 0.24 0.14
## incongruent_errors 1.20 0.58 0.14
## neuroticism -0.09 -0.97 0.14
## switch_cost 0.37 -0.30 0.14
## terrible_codriver 1.67 3.20 0.14
##
## Call:
## lm(formula = tmp$distance_overall_deviation ~ ., data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2372.8 -630.8 -122.1 629.1 3584.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 962.4 163.1 5.899 4.1e-07 ***
## confidence 283.5 177.3 1.599 0.116772
## incongruent_errors 271.2 179.8 1.508 0.138327
## neuroticism 564.5 176.5 3.199 0.002498 **
## switch_cost -227.2 194.8 -1.166 0.249530
## terrible_codriver 700.5 168.1 4.168 0.000134 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1176 on 46 degrees of freedom
## Multiple R-squared: 0.4709, Adjusted R-squared: 0.4134
## F-statistic: 8.187 on 5 and 46 DF, p-value: 1.364e-05
##
## Correlations
## -----------------------------------------------------
## Variable Zero Order Partial Part
## -----------------------------------------------------
## confidence 0.260 0.229 0.171
## incongruent_errors 0.303 0.217 0.162
## neuroticism 0.366 0.427 0.343
## switch_cost -0.341 -0.169 -0.125
## terrible_codriver 0.404 0.524 0.447
## -----------------------------------------------------
##
## Call:
## omcdiag(x = x, y = y)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.6548 0
## Farrar Chi-Square: 20.3218 1
## Red Indicator: 0.2017 0
## Sum of Lambda Inverse: 5.9389 0
## Theil's Method: -1.1319 0
## Condition Number: 14.7394 0
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## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
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## Call:
## imcdiag(x = x, y = y)
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## All Individual Multicollinearity Diagnostics Result
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## VIF TOL Wi Fi Leamer CVIF Klein
## confidence 1.1589 0.8629 1.8666 2.5418 0.9289 1.4362 0
## incongruent_errors 1.1920 0.8389 2.2563 3.0724 0.9159 1.4773 0
## neuroticism 1.1477 0.8713 1.7357 2.3635 0.9334 1.4224 0
## switch_cost 1.3993 0.7147 4.6914 6.3883 0.8454 1.7341 0
## terrible_codriver 1.0410 0.9606 0.4816 0.6558 0.9801 1.2901 0
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## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
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## confidence , incongruent_errors , switch_cost , coefficient(s) are non-significant may be due to multicollinearity
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## R-square of y on all x: 0.4709
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## * use method argument to check which regressors may be the reason of collinearity
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## NULLInvestigate distance deviation outliers
Not sure why the regression results change so much when any of these outliers are removed. Need to investigate further.
When did the deviation occur for each of the outlier teams? 
All teams had good network connections. The codrivers all saw the traffic experienced by the drivers and the arrows appeared correctly on the codrivers screens.
For teams 16110214_1 and 17081510_2 a huge deviation occurred during lap 4. I have checked the screen capture videos for these 2 teams.
For team 16110214_1 the codriver paid no attention to the arrows throughout the whole drive. They were often saying “keep going straight” regardless of the direction the arrows pointed. During lap 4 they directed the driver the wrong way twice at the same black ice intersection. This sent the driver on a large loop. The first time they told the driver to turn right (instead of left), the second time they directed the driver straight and the third time the driver was directed left. The driver listened to the codriver’s direction even when they could see it was wrong. For substantial periods of lap 4 the codriver was not following the driver with the drone but they continued to direct and offer info/instruction. The codriver clearly did not follow instructions. This team should be high on inconsistent_codriver and maybe terrible codriver.
For team 17081510_2 the misdirection occurred at the same black ice intersection in lap 4 although this time it was a design flaw that sent the driver the wrong way. They entered the intersection with the intention of turning right but when they lost control on the black ice and overshot the turn they could see green lights in the distance for the next intersection and they were pointing straight so they continued in that direction. This happened exactly the same a second time when they overshot the black ice intersection. On the third attempt to turn left they did not overshoot it and made it. It seems the driver was following instructions but a design flaw sent them the wrong way. This should not influence the teams scores on the comms factors.
For team 16101114_1 large deviations occured in laps 1 and 5. During lap 1 the driver was not doing a good job of monitoring the arrows and made a number of wrong turns that went uncorrected because the codriver was also not paying attention to the arrows. During lap 5 the driver missed a turn (again they didn’t notice) and when the codriver tried to correct them the driver could see arrows at the next intersection and decided to continue in the wrong direction. This team should score positively on helpful codriver.
Let’s check the comms factor scores for each of these teams.
## # A tibble: 3 x 4
## team inconsistent_codriver terrible_codriver helpful_exchange
## <chr> <dbl> <dbl> <dbl>
## 1 16101114_1 -0.656 -0.925 0.855
## 2 16110214_1 0.351 3.60 -0.795
## 3 17032409_1 -0.297 0.126 1.11I recoded the comms for 16110214_1. How do the new scores on the comms variables and comms factors compare to the original scores.
## [1] "New scores"## # A tibble: 16 x 2
## var val
## <chr> <dbl>
## 1 inconsistent_codriver 0.351
## 2 terrible_codriver 3.60
## 3 helpful_exchange -0.795
## 4 co_info_help_overall 21
## 5 co_info_harm_overall 14
## 6 co_instruct_help_overall 70
## 7 co_instruct_harm_overall 22
## 8 co_total_help_overall 91
## 9 co_total_harm_overall 36
## 10 co_redundant_overall 33
## 11 co_question_overall 3
## 12 co_total_overall 163
## 13 drive_question_overall 42
## 14 drive_informs_overall 31
## 15 drive_frust_overall 35
## 16 drive_total_overall 73## [1] "Original scores"## # A tibble: 16 x 2
## var val
## <chr> <dbl>
## 1 inconsistent_codriver 1.33
## 2 terrible_codriver 2.37
## 3 helpful_codriver -1.47
## 4 co_info_help_overall 20
## 5 co_info_harm_overall 12
## 6 co_instruct_help_overall 79
## 7 co_instruct_harm_overall 29
## 8 co_total_help_overall 99
## 9 co_total_harm_overall 41
## 10 co_redundant_overall 17
## 11 co_question_overall 3
## 12 co_total_overall 160
## 13 drive_question_overall 40
## 14 drive_informs_overall 32
## 15 drive_frust_overall 29
## 16 drive_total_overall 72For team 17081512_2 there was no large deviation so they were not an outlier on the DV (distance deviation). Maybe they were an outlier on the comms factors. Let’s take a look.
17081512_2 were initially identified as having a large influence on the distance deviation results because they had a very high score on the terrible codriver factor. I reviewed the coding and made some changes. How do the new scores on the comms variables and comms factors compare to the original scores.
## [1] "New scores"## # A tibble: 16 x 2
## var val
## <chr> <dbl>
## 1 inconsistent_codriver -0.820
## 2 terrible_codriver 1.30
## 3 helpful_exchange 0.868
## 4 co_info_help_overall 81
## 5 co_info_harm_overall 7
## 6 co_instruct_help_overall 48
## 7 co_instruct_harm_overall 3
## 8 co_total_help_overall 129
## 9 co_total_harm_overall 10
## 10 co_redundant_overall 27
## 11 co_question_overall 1
## 12 co_total_overall 167
## 13 drive_question_overall 32
## 14 drive_informs_overall 26
## 15 drive_frust_overall 30
## 16 drive_total_overall 58## [1] "Original scores"## # A tibble: 16 x 2
## var val
## <chr> <dbl>
## 1 inconsistent_codriver -0.991
## 2 terrible_codriver 4.10
## 3 helpful_codriver 1.09
## 4 co_info_help_overall 78
## 5 co_info_harm_overall 22
## 6 co_instruct_help_overall 70
## 7 co_instruct_harm_overall 2
## 8 co_total_help_overall 148
## 9 co_total_harm_overall 24
## 10 co_redundant_overall 51
## 11 co_question_overall 1
## 12 co_total_overall 224
## 13 drive_question_overall 40
## 14 drive_informs_overall 33
## 15 drive_frust_overall 33
## 16 drive_total_overall 73Investigate speed outliers
I recoded the comms for each of the speed outliers because the results were different with each outlier removed separately. How do the new scores on the comms variables and comms factors compare to the original scores.
## [1] "New scores"## # A tibble: 3 x 4
## team inconsistent_codriver terrible_codriver helpful_exchange
## <chr> <dbl> <dbl> <dbl>
## 1 17040414_2 -1.17 -1.37 -1.11
## 2 17040711_1 -0.414 -0.914 0.297
## 3 17080810_2 0.0783 0.348 -0.872## [1] "Original scores"## # A tibble: 3 x 4
## team inconsistent_codriver terrible_codriver helpful_codriver
## <chr> <dbl> <dbl> <dbl>
## 1 17040414_2 -1.21 -1.20 -1.10
## 2 17040711_1 -0.151 -0.672 -0.426
## 3 17080810_2 0.495 0.481 -0.973